- [2015-PAMI] Text Detection and Recognition in Imagery: A Survey
paper
- [2014-Front.Comput.Sci] Scene Text Detection and Recognition: Recent Advances and Future Trends
paper
- [2017-ICCV]Single Shot TextDetector with Regional Attention [Paper]
- [2017-ICCV]WordSup: Exploiting Word Annotations for Character based Text Detection [Paper]
- [2017-arXiv]R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection[Paper]
- [2017-CVPR]EAST: An Efficient and Accurate Scene Text Detector [Paper] [Code]
- [2017-arXiv]Cascaded Segmentation-Detection Networks for Word-Level Text Spotting[Paper]
- [2017-arXiv]Deep Direct Regression for Multi-Oriented Scene Text Detection [Paper]
- [2017-CVPR]Detecting oriented text in natural images by linking segments [Paper]
- [2017-CVPR]Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection [Paper]
- [2017-arXiv]Arbitrary-Oriented Scene Text Detection via Rotation Proposals [Paper]
- [2017-AAAI]TextBoxes: A Fast Text Detector with a Single Deep Neural Network[Paper][Code]
- [2016-arXiv]Accurate Text Localization in Natural Image with Cascaded Convolutional TextNetwork [Paper]
- [2016-arXiv]DeepText : A Unified Framework for Text Proposal Generation and Text Detectionin Natural Images [Paper] [Data]
- [2017-PR]TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild [paper] [code]
- [2016-arXiv] Scene Text Detection via Holistic, Multi-Channel Prediction [Paper]
- [2016-CVPR] CannyText Detector: Fast and Robust Scene Text Localization Algorithm [Paper]
- [2016-CVPR]Synthetic Data for Text Localisation in Natural Images[Paper] [Data] [Code]
- [2016-ECCV]Detecting Text in Natural Image with Connectionist Text Proposal Network[Paper] [Demo][Code]
- [2016-TIP]Text-Attentional Convolutional Neural Networks for Scene Text Detection[Paper]
- [2016-IJDAR]TextCatcher: a method to detect curved and challenging text in natural scenes[Paper]
- [2016-CVPR]Multi-oriented text detection with fully convolutional networks[Paper]
- [2015-TPRMI]Real-time Lexicon-free Scene Text Localization and Recognition
- [2015-CVPR]Symmetry-Based Text Line Detection in Natural Scenes
- [2015-ICCV]FASText: Efficient unconstrained scene text detector [Paper] https://github.com/MichalBusta/FASText
- [2015-D.PhilThesis] Deep Learning for Text Spotting [Paper]
- [2015 ICDAR]Object Proposals for Text Extraction in the Wild [Paper] https://github.com/lluisgomez/TextProposals
- [2014-ECCV] Deep Features for Text Spotting [Paper] https://bitbucket.org/jaderberg/eccv2014_textspotting https://bitbucket.org/jaderberg/eccv2014_textspotting http://gitxiv.com/posts/uB4y7QdD5XquEJ69c/deep-features-for-text-spotting
- [2014-TPAMI] Word Spotting and Recognition with Embedded Attributes [Paper] http://www.cvc.uab.es/~almazan/index/projects/words-att/index.html https://github.com/almazan/watts
- [2014-TPRMI]Robust Text Detection in Natural Scene Images
- [2014-ECCV] Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees [Paper]
- [2013-ICCV] Photo OCR: Reading Text in Uncontrolled Conditions [Paper]
- [2012-CVPR]Real-time scene text localization and recognition [Paper]
- [2010-CVPR]Detecting Text in Natural Scenes with Stroke Width Transform [Paper]
- [2017-arvix ] Full-Page TextRecognition : Learning Where to Start and When to Stop[ href="https://arxiv.org/pdf/1704.08628.pdf
- [2016-AAAI]Reading Scene Text in Deep Convolutional Sequences [Paper]
- [2016-IJCV]Reading Text in the Wild with Convolutional Neural Networks [Paper] http://zeus.robots.ox.ac.uk/textsearch/#/search/ http://www.robots.ox.ac.uk/~vgg/research/text
- [2016-CVPR]Recursive Recurrent Nets with Attention Modeling for OCR in the Wild [Paper]
- [2016-CVPR] Robust Scene Text Recognition with Automatic Rectification [Paper]
- [2016-NIPs] Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data [Paper]
- [2015-CoRR] AnEnd-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition [Paper] https://github.com/bgshih/crnn
- [2015-ICDAR]Automatic Script Identification in the Wild [Paper]
- [2015-ICLR] Deep structured output learning for unconstrained text recognition [Paper]
- [2014-NIPS]Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition [Paper] http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14c/ http://www.robots.ox.ac.uk/~vgg/research/text/model_release.tar.gz
- [2014-TIP] A Unified Framework for Multi-Oriented Text Detection and Recognition
- [2012-ICPR]End-to-End Text Recognition with Convolutional Neural Networks [Paper] http://cs.stanford.edu/people/twangcat/ICPR2012_code/SceneTextCNN_demo.tar http://ufldl.stanford.edu/housenumbers/
COCO-Text (ComputerVision Group, Cornell) 2016
- 63,686images, 173,589 text instances, 3 fine-grained text attributes.
- Task:text location and recognition
Synthetic Data for Text Localisation in Natural Image (VGG)2016
- 800k thousand images
- 8 million synthetic word instances
- download
Synthetic Word Dataset (Oxford, VGG) 2014
- 9million images covering 90k English words
- Task:text recognition, segmentation
- download
- 5000images from Scene Texts and born-digital (2k training and 3k testing images)
- Eachimage is a cropped word image of scene text with case-insensitive labels
- Task:text recognition
- download
StanfordSynth(Stanford, AI Group) 2012
- Small single-character images of 62 characters (0-9, a-z, A-Z)
- Task:text recognition
- download
MSRA Text Detection 500 Database(MSRA-TD500) 2012
- 500 natural images(resolutions of the images vary from 1296x864 to 1920x1280)
- Chinese,English or mixture of both
- Task:text detection
- 350 high resolution images (average size 1260 × 860) (100 images for training and 250 images for testing)
- Only word level bounding boxes are provided with case-insensitive labels
- Task:text location
KAIST Scene_Text Database 2010
- 3000 images of indoor and outdoor scenes containing text
- Korean,English (Number), and Mixed (Korean + English + Number)
- Task:text location, segmentation and recognition
-
Over 74K images from natural images, as well as a set of synthetically generatedcharacters
-
Smallsingle-character images of 62 characters (0-9, a-z, A-Z)
-
Task:text recognition
-
ICDAR Benchmark Datasets
Dataset | Discription | Competition Paper |
---|---|---|
ICDAR 2015 | 1000 training images and 500 testing images | paper |
ICDAR 2013 | 229 training images and 233 testing images | paper |
ICDAR 2011 | 229 training images and 255 testing images | paper |
ICDAR 2005 | 1001 training images and 489 testing images | paper |
ICDAR 2003 | 181 training images and 251 testing images(word level and character level) | paper |
- Scene Text Detection with OpenCV 3
- Handwritten numbers detection and recognition
- Applying OCR Technology for Receipt Recognition
- Convolutional Neural Networks for Object(Car License) Detection
- Extracting text from an image using Ocropus
- Number plate recognition with Tensorflow
github
- Using deep learning to break a Captcha system
report
github
- Breaking reddit captcha with 96% accuracy
github
- Tesseract c++ based tools for documents analysis and OCR [code]
- Ocropy: Python-based tools for document analysis and OCR [ href="https://github.com/tmbdev/ocropy">code]
- CLSTM : A small C++ implementation of LSTM networks,focused on OCR [ href="https://github.com/tmbdev/clstm">code]
- Convolutional Recurrent Neural Network,Torch7 based [ href="https://github.com/bgshih/crnn">code]
- Attention-OCR: Visual Attention based OCR [ href="https://github.com/da03/Attention-OCR">code]
- Umaru: An OCR-system based on torch using the technique of LSTM/GRU-RNN, CTC and referred to the works of rnnlib and clstm [ href="https://github.com/edward-zhu/umaru">code]
- AKSHAYUBHAT/DeepVideoAnalytics (CTPN+CRNN) code
- ankush-me/SynthText code
- JarveeLee/SynthText_Chinese_version code
- DeepFont:Identify Your Font from An Image [Paper]
- Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks [Paper]
- End-to-End Interpretation of the French Street Name Signs Dataset [ href="http://link.springer.com/chapter/10.1007%2F978-3-319-46604-0_30"> [ href="https://github.com/tensorflow/models/tree/master/street">
- Extracting text from an image using Ocropus [ href="http://www.danvk.org/2015/01/09/extracting-text-from-an-image-using-ocropus.html">blog]
- [2016-arXiv]Drawingand Recognizing Chinese Characters with Recurrent Neural Network [ href="https://arxiv.org/abs/1606.06539
- Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition [ href="https://arxiv.org/abs/1610.02616
- Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition [ href="https://arxiv.org/abs/1610.04057
- High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps [ href="http://arxiv.org/abs/1505.04925">
- DeepHCCR:Offline Handwritten Chinese Character Recognition based on GoogLeNet and AlexNet (With CaffeModel) [ href="https://github.com/chongyangtao/DeepHCCR">
- Scan,Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTMAttention [ href="http://arxiv.org/abs/1604.03286
- MLPaint:the Real-Time Handwritten Digit Recognizer [ href="http://blog.mldb.ai/blog/posts/2016/09/mlpaint/">blog][ href="https://github.com/mldbai/mlpaint"> [ href="https://docs.mldb.ai/ipy/notebooks/_demos/_latest/Image%20Processing%20with%20Convolutions.html">demo]
- caffe-ocr: OCR with caffe deep learning framework [ href="https://github.com/pannous/caffe-ocr">code] (单字分类器)
- ReadingCar License Plates Using Deep Convolutional Neural Networks and LSTMs ; [ href="
- Numberplate recognition with Tensorflow [ href="http://matthewearl.github.io/2016/05/06/cnn-anpr/">blog]
- end-to-end-for-plate-recognition[ href="https://github.com/szad670401/end-to-end-for-chinese-plate-recognition">
- ApplyingOCR Technology for Receipt Recognition[ href="http://rnd.azoft.com/applying-ocr-technology-receipt-recognition/">blog][ href="http://pan.baidu.com/s/1qXQBQiC">mirror]
- [2017-Arvix]Using Synthetic Data to Train NeuralNetworks is Model-Based Reasoning[ href="https://arxiv.org/pdf/1703.00868.pdf">
- Using deep learning to break a Captcha system [ href="https://deepmlblog.wordpress.com/2016/01/03/how-to-break-a-captcha-system/">blog]
- Breakingreddit captcha with 96% accuracy [ href="https://deepmlblog.wordpress.com/2016/01/05/breaking-reddit-captcha-with-96-accuracy/">blog]
- I'm not a human: Breaking the Google reCAPTCHA (https://www.blackhat.com/docs/asia-16/materials/asia-16-Sivakorn-Im-Not-a-Human-Breaking-the-Google-reCAPTCHA-wp.pdf)
- NeuralNet CAPTCHA Cracker [ href="http://www.cs.sjsu.edu/faculty/pollett/masters/Semesters/Spring15/geetika/CS298%20Slides%20-%20PDF">slides] [ href="https://github.com/bgeetika/Captcha-Decoder"> [ href="http://cp-training.appspot.com/">demo]
- Recurrentneural networks for decoding CAPTCHAS [ href="https://deepmlblog.wordpress.com/2016/01/12/recurrent-neural-networks-for-decoding-captchas/">blog] [ href="http://sourceforge.net/projects/simplecaptcha/"> [ href="http://simplecaptcha.sourceforge.net/">demo]
- Readingirctc captchas with 95% accuracy using deep learning [ href="https://github.com/arunpatala/captcha.irctc">
- End-to-EndOCR:based on CNN [ href="http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/">blog]
- IAm Robot: (Deep) Learning to Break Semantic Image CAPTCHAs [ href="http://www.cs.columbia.edu/~polakis/papers/sivakorn_eurosp16.pdf